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Bechtel Corporation Interview Questions

Bechtel Corporation Interview Guides

Machine Learning

Bank Fraud Model

Bagging vs Boosting

Food Delivery Times

Decision Tree Evaluation

How to model merchant acquisition in a new market?

Merchant Acquisition

Product Sense & Metrics

Group Success

Instagram TV Success

Amateur Performance

Docs Metrics

WhatsApp Metrics

Data Structures & Algorithms

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

Sort Strings

This problem involves identifying duplicate numbers in a list of integers. The function should return a list of the duplicate numbers.

Find Duplicate Numbers in a List

Append Frequency

Digit Accumulator

Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

Employee Salaries

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Time Difference

Total Time in Flight

Write a query that outputs a random manufacturer's name with an equal probability of selecting any name.

Uniform Car Maker

Behavioral

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

Describing a data project and its challenges

Hurdles In Data Projects

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

Making data-driven insights actionable for those without technical expertise

Simple Explanations

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Given a <code>employees</code> and <code>departments</code> table, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>percentage_over_100k</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>number_of_employees</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.

Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def prime_numbers(N) -&gt; [2,3]
</code></pre>

Given a list of integers, identify all the duplicate values in the list. Assume that the list can contain both positive and negative numbers, and the order of the list does not matter. A number is considered a duplicate if it appears more than once in the list. Return a list of the duplicate numbers.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 2, 3, 1, 2, 3]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>find_duplicates(nums) -&gt; [1, 2, 3]
</code></pre>

The numbers 1, 2, and 3 all appear more than once in the list, so they are considered duplicates.

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, -1, 2, 3, 3, -1]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>find_duplicates(nums) -&gt; [-1, 3]
</code></pre>

The numbers -1 and 3 both appear more than once in the list, so they are considered duplicates. Note that the order of the output does not matter.

Example 3:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 2, 3, 4, 5]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>find_duplicates(nums) -&gt; []
</code></pre>

None of the numbers in the list appear more than once, so there are no duplicates.

You’re given a string that may contain the characters <code>{</code>, <code>}</code>, <code>[</code>, <code>]</code>, <code>(</code>, and <code>)</code>.

Task: Verify that the string is balanced. A balanced string is one where every opening character, <code>{</code>, <code>[</code>, or <code>(</code>, has a corresponding closing character, <code>}</code>, <code>]</code>, or <code>)</code>.

Write a function called <code>is_balanced(string: str) -&gt; bool</code> which verifies the balance of a string.

Example:

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;(())[]{}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{([(){}])()}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{}[]())&#39;) -&gt; False
</code></pre>

<hr/>

Write a function that tests whether a string of brackets is balanced.

The Brackets Problem

You’re a data scientist at YouTube. Leadership wants to revisit how the platform measures success, not for a specific new feature, but for the product as a whole given its current state.

How would you define and measure success for YouTube? Identify your primary and secondary metrics, explain your rationale for each, and discuss how you would choose the right thresholds for any engagement-based metrics you propose.

The role of A/B testing in measuring the success rate of an analytics experiment

Success Measurement

You are given a binary tree of unique positive numbers. Each node in the binary tree is represented as a dictionary with the following keys:

<ul>
<li><code>data</code>: integer value stored in the node</li>
<li><code>left</code>: reference to the left child (or <code>None</code>)</li>
<li><code>right</code>: reference to the right child (or <code>None</code>)</li>
</ul>

<pre tabindex="0" class="chroma"><code>node = {
 &#34;data&#34;: 6,
 &#34;left&#34;: {
 &#34;data&#34;: 3,
 &#34;left&#34;: {...},
 &#34;right&#34;: {...}
 },
 &#34;right&#34;: {
 &#34;data&#34;: 9,
 &#34;left&#34;: {...},
 &#34;right&#34;: {...}
 }
}
</code></pre>

Given two node values as input (<code>value1</code> and <code>value2</code>), write a function to return the value of the nearest common ancestor (lowest node in the tree that has both nodes as descendants).

Note: If one of the nodes doesn’t exist in the tree, return <code>-1</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code># Diagram of the binary tree
&#39;&#39;&#39;
 6 
 / \ 
 3 9 
 / \
 2 11
 / \
 5 8
&#39;&#39;&#39;
value1 = 8
value2 = 2
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>common_ancestor(root,value1,value2) -&gt; 3
</code></pre>

Explanation:

<ul>
<li>Ancestors of <code>8</code>: <code>11 → 3 → 6</code></li>
<li>Ancestors of <code>2</code>: <code>3 → 6</code></li>
<li>Common ancestors: <code>3</code> and <code>6</code></li>
<li>The nearest common ancestor is <code>3</code>.</li>
</ul>

Write a function to return the value of the nearest node that is a parent to both nodes.

Nearest Common Ancestor

You’re AI engineer working on an energy forecasting system at Siemens that helps utilities predict short-term electricity demand to balance grid load and reduce outages. You train a recurrent neural network on long historical time series (weather, calendar effects, local demand signals) to forecast energy demand.

Early in training, the model appears to learn normally, but after several epochs the loss becomes highly unstable, oscillating wildly before turning into NaNs. At the same time, training slows dramatically and the model fails to recover even after restarting from recent checkpoints.

You’ve verified that the data pipeline is correct and labels are well-aligned with inputs. No changes were made to the dataset, but increasing the sequence length or learning rate makes the problem significantly worse.

How would you investigate the cause of this training instability? What signals would you look for during debugging, and how would those findings guide the changes you make to the model or training setup?

How would you investigate the cause of this training instability?

Training Instability in Neural Networks

Given a string <code>sentence</code>, return the same string with an addendum after each character of the number of occurrences a character appeared in the <code>sentence</code>.

Note: don’t treat the spaces <code>&#34; &#34;</code> as characters. Also; do not return the addendum for those characters that also appear in the <code>discard_list</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>sentence = &#34;Interview Query&#34;

discard_list = [&#39;I&#39;,&#39;e&#39;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>inject_frequency(sentence, discard_list) -&gt; &#34;In1t1er2v1i1ew1 Q1u1er2y1&#34;
</code></pre>

How would you handle the data preparation for building a machine learning model using imbalanced data?

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Given a list of strings, write a function, <code>sorting</code> from scratch to sort the list in ascending alphabetical order.

Notes:
- Do NOT use the built-in <code>sorted</code> function
- Return the new sorted list, rather than modifying the list in-place

Bonus: Have your solution be \(O(n log(n))\).

Example:

Input:

<pre tabindex="0" class="chroma"><code>array = [&#34;apple&#34;, &#34;cat&#34;, &#34;banana&#34;, &#34;zoo&#34;, &#34;football&#34;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sorting(array) -&gt; [&#34;apple&#34;, &#34;banana&#34;, &#34;cat&#34;, &#34;football&#34;, &#34;zoo&#34;]
</code></pre>

Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.

How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?

Bias vs. Variance Tradeoff

Let’s say you work in air traffic control. You are given the table below containing information on flights between two cities.

Write a query to find out how much time, in minutes (rounded down), each plane spent in the air each day.

Note: Both cities are in the same time zone, so you do not need to worry about converting time zones.

<code>flights</code> table

Example:

Input:

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>destination_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>source_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>plane_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>flight_start</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>flight_end</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>plane_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>calendar_day</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>time_in_min</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Please give <code>calendar_day</code> in the format <code>YYYY-MM-DD</code>

Given a table of <code>cars</code> with columns <code>id</code> and <code>make</code>, write a query that outputs a random manufacturer’s name with an equal probability of selecting any name.

Input:

<code>cars</code> table

<table>
<thead>
<tr>
<th><code>id</code></th>
<th><code>make</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>Ford</td>
</tr>

<tr>
<td>2</td>
<td>Toyota</td>
</tr>

<tr>
<td>3</td>
<td>Toyota</td>
</tr>

<tr>
<td>4</td>
<td>Honda</td>
</tr>

<tr>
<td>5</td>
<td>Honda</td>
</tr>

<tr>
<td>6</td>
<td>Honda</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>make</code></td>
<td>Text</td>
</tr>
</tbody>
</table>

Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

Statistics

You work at a ride-sharing company and are given a table containing information about the rides of users.

Write a query to get the duration (in minutes) of each ride longer than two hours. Sort the results by duration in descending order.

<code>rides</code> table:

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>passenger_user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>start_dt</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_dt</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>distance</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>ride_region</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>is_completed</code>	</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column	</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>duration_minutes</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Let’s say you have analytics data stored in a data lake. An analyst tells you they need hourly, daily, and weekly active user data for a dashboard that refreshes every hour.

How would you build this data pipeline?

Design a data pipeline for hourly user analytics.

Data Pipelines and Aggregation

Data Pipelines

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

Let’s say we’re managing a production line for assembling electric scooters, with 10 stations where each has a different average processing time per unit (for example, Station 1 takes 5 minutes, Station 2 takes 3 minutes, and so on).

How would you determine the right metric to use for balancing worker allocation across the stations, and how would you use it to figure out how many workers each station needs?

How would you decide on a metric and approach for worker allocation across an uneven production line?

Worker Distribution Dilemma

Supply Chain

Let’s say that you’re a data scientist working for Facebook.

You’ve been asked to generate a machine learning model that can map the legal first name of a person to likely nicknames they might have.

How do you go about designing this model?

Mapping Nicknames

You are given a <code>string</code> that represents some floating-point number. Write a function, <code>digit_accumulator</code>, that returns the sum of every digit in the <code>string</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>s = &#34;123.0045&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def digit_accumulator(s) -&gt; 15

Since 
1 + 2 + 3 + 0 + 0 + 4 + 5 = 15
</code></pre>

How would you measure success for Facebook Stories?

Measure Facebook Stories success by tracking reach, engagement, and actions aligned with specific business goals

Facebook Story Success

To improve customer experience on Uber Eats, what key parameters would you focus on improving?

Delivering an exceptional customer experience by focusing on key customer-centric parameters

Uber Eats Customer Experience

Let’s say we have a search form in our web app where users can enter a keyword like “star”, and the system searches for matches across several different fields in a large database table. Lately, these searches have been timing out when users search for common words.

How would you approach diagnosing and resolving these search timeouts?

How would you resolve slow, timing-out keyword searches across multiple database fields?

Search Timeout

When an interviewer asks you a question along the lines of:

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

How should you respond?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

Let’s say you work as a data scientist at a bank.

You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan they are taking out.

<ol>
<li>How would you evaluate whether using a decision tree algorithm is the correct model for the problem?</li>

<li>Let’s say you move forward with the decision tree model. How would you evaluate the performance of the model before deployment and after?</li>
</ol>

Describe a data project you worked on. What were some of the challenges you faced?

You have been tasked with designing three classes: <code>text_editor</code>, <code>moving_text_editor</code>, and <code>smart_text_editor</code>. These classes are to be created with specific functionalities as defined below:

<ol>
<li><code>text_editor</code> class:

<ul>
<li><code>write_line(string:str)</code>: A method which appends a given string to the end of the existing string.</li>
<li><code>delete_line(char_num : int)</code>: A method which deletes <code>char_num</code> number of characters from the existing string, starting from the end. If there are no characters left, the method should do nothing.</li>
<li><code>special_operation()</code>: A method which currently does nothing.</li>
<li><code>get_notes()</code>: A method which returns the internal string.</li>
</ul></li>

<li><code>moving_text_editor</code> class:

<ul>
<li>This class extends <code>text_editor</code>. The <code>special_operation()</code> method is overridden. Initially, the cursor will be at the end of the current string. If <code>special_operation()</code> is called, it moves the cursor to the beginning of the string, any additional appends will be appended to the beginning of the string instead. Calling <code>special_operation()</code> again reverses the cursor operation.</li>
</ul></li>

<li><code>smart_text_editor</code> class:

<ul>
<li>This class extends <code>text_editor</code>. In this class, the <code>special_operation()</code> method is overridden to serve as an undo operation, allowing it to undo an infinite number of operations.</li>
</ul></li>
</ol>

<h3>Input</h3>

<pre tabindex="0" class="chroma"><code>[[&#39;special_text_editor&#39;], [&#39;write_line&#39;, &#39;special_operation&#39;, &#39;write_line&#39;], [&#39;World&#39;, &#39;Hello, &#39;]]
</code></pre>

<h3>Output</h3>

<code>&#34;Hello, World&#34;</code>

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

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